Issue Archive

Noise fault detector detects an unreasonably high or low variance or standard deviation.

A Sensor Data Qualification (SDQ)
function has been developed that allows
the onboard flight computers on NASA’s
launch vehicles to determine the validity of
sensor data to ensure that critical safety
and operational decisions are not based on
faulty sensor data. This SDQ function
includes a novel noise series fault detection
algorithm for qualification of the output
data from LO2 and LH2 low-level liquid
sensors. These sensors are positioned in a
launch vehicle’s propellant tanks in order
to detect propellant depletion during a
rocket engine’s boost operating phase.
This detection capability can prevent the
catastrophic situation where the engine
operates without propellant. The output
from each LO2 and LH2 low-level liquid
sensor is a discrete valued signal that is
expected to be in either of two states,
depending on whether the sensor is
immersed (wet) or exposed (dry).
Conventional methods for sensor data
qualification, such as threshold limit checking,
are not effective for this type of signal
due to its discrete binary-state nature.

To address this data qualification challenge,
a noise computation and evaluation
method, also known as a noise fault
detector, was developed to detect unreasonable
statistical characteristics in the
discrete data stream. The method operates
on a time series of discrete data
observations over a moving window of
data points and performs a continuous
examination of the resulting observation
stream to identify the presence of
anomalous characteristics. If the
method determines the existence of
anomalous results, the data from the
sensor is disqualified for use by other
monitoring or control functions.

This work was done by Christopher
Fulton, Edmond Wong, and Kevin Melcher
of Glenn Research Center; and Randall
Bickford of Expert Microsystems, Inc. For
more information, contact kimberly.a.
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